# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask

# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")

nltk.download('all')

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[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all

True

# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type
0	1	Snowpiercer	2013	18+	6.9	94%	NaN	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	English	Set seven years after the world has become a f...	60.0	tv series	3.0	1	0	0	0	1
1	2	Philadelphia	1993	13+	8.8	80%	NaN	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	English	The gang, 5 raging alcoholic, narcissists run ...	22.0	tv series	18.0	1	0	0	0	1
2	3	Roma	2018	18+	8.7	93%	NaN	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	English	In this British historical drama, the turbulen...	52.0	tv series	2.0	1	0	0	0	1
3	4	Amy	2015	18+	7.0	87%	NaN	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	English	A family drama focused on three generations of...	60.0	tv series	6.0	1	0	1	1	1
4	5	The Young Offenders	2016	NaN	8.0	100%	NaN	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	English	NaN	30.0	tv series	3.0	1	0	0	0	1

# profile = ProfileReport(df_tvshows)
# profile

def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation : 

# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation : 

df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	Set seven years after the world has become a f...	60	tv series	3	1	0	0	0	1	Netflix
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	...	The gang, 5 raging alcoholic, narcissists run ...	22	tv series	18	1	0	0	0	1	Netflix
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	In this British historical drama, the turbulen...	52	tv series	2	1	0	0	0	1	Netflix
3	4	Amy	2015	18	7	87	NA	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	...	A family drama focused on three generations of...	60	tv series	6	1	0	1	1	1	Netflix
4	5	The Young Offenders	2016	NR	8	100	NA	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	...	NA	30	tv series	3	1	0	0	0	1	Netflix

5 rows × 21 columns

df_tvshows.describe()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.0
mean	2716.500000	2010.668446	0.341311	0.293999	0.403351	0.033689	1.0
std	1568.227662	11.726176	0.474193	0.455633	0.490615	0.180445	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	1.0
25%	1358.750000	2009.000000	0.000000	0.000000	0.000000	0.000000	1.0
50%	2716.500000	2014.000000	0.000000	0.000000	0.000000	0.000000	1.0
75%	4074.250000	2017.000000	1.000000	1.000000	1.000000	0.000000	1.0
max	5432.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	1.0

df_tvshows.corr()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.031346	-0.646330	0.034293	0.441264	0.195409	NaN
Year	-0.031346	1.000000	0.222316	-0.065807	-0.198675	-0.022741	NaN
Netflix	-0.646330	0.222316	1.000000	-0.366515	-0.515086	-0.119344	NaN
Hulu	0.034293	-0.065807	-0.366515	1.000000	-0.377374	-0.075701	NaN
Prime Video	0.441264	-0.198675	-0.515086	-0.377374	1.000000	-0.151442	NaN
Disney+	0.195409	-0.022741	-0.119344	-0.075701	-0.151442	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)

# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows

# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]

df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]

df_tvshows_age = df_tvshows.copy()

df_tvshows_age.drop(df_tvshows_age.loc[df_tvshows_age['Age'] == "NA"].index, inplace = True)
df_tvshows_age.drop(df_tvshows_age.loc[df_tvshows_age['Age'] == "NR"].index, inplace = True)
# df_tvshows_age = df_tvshows_age[df_tvshows_age.Age != "NA"]
df_tvshows_age['Age'] = df_tvshows_age['Age'].astype(int)

# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Netflix'] == 1]
hulu_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Hulu'] == 1]
prime_video_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Prime Video'] == 1]
disney_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Disney+'] == 1]

df_tvshows_age_group = df_tvshows_age.copy()

plt.figure(figsize = (10, 10))
corr = df_tvshows_age.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>

df_age_all_tvshows = df_tvshows_age

print('\nTV Shows with Age Rating are : \n')
df_age_all_tvshows.head(5)

TV Shows with Age Rating are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	Set seven years after the world has become a f...	60	tv series	3	1	0	0	0	1	Netflix
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	...	The gang, 5 raging alcoholic, narcissists run ...	22	tv series	18	1	0	0	0	1	Netflix
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	In this British historical drama, the turbulen...	52	tv series	2	1	0	0	0	1	Netflix
3	4	Amy	2015	18	7	87	NA	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	...	A family drama focused on three generations of...	60	tv series	6	1	0	1	1	1	Netflix
8	9	Quincy	2018	18	7.3	82	NA	Jack Klugman,John S. Ragin,Robert Ito,Joseph R...	Crime,Drama,Mystery,Thriller	United States	...	Quincy and Sam are working as Coroners. Inspec...	60	tv series	8	1	0	0	0	1	Netflix

5 rows × 21 columns

df_age_0_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 0]

print('\nTV Shows with All Age Rating are : \n')
df_age_0_tvshows.head(5)

TV Shows with All Age Rating are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
24	25	Kung Fu Panda Holiday	2010	0	6.8	NA	Tim Johnson	Jack Black,Dustin Hoffman,Angelina Jolie,Seth ...	Animation,Short,Action,Comedy,Family	United States	...	The Winter Festival is coming and Po is asked ...	21	tv series	NA	1	0	0	0	1	Netflix
29	30	Zapped	2014	0	6.8	6	Peter DeLuise	James Buckley,Kenneth Collard,Louis Emerick,Pa...	Comedy,Fantasy	United Kingdom	...	NA	30	tv series	3	1	1	0	1	1	Netflix
62	63	True: Happy Hearts Day	2019	0	8.3	NA	Harold Harris	Michela Luci,Jamie Watson,Eric Peterson,Anna C...	Animation,Short,Adventure,Family,Fantasy	NA	...	NA	NA	tv series	NA	1	0	0	0	1	Netflix
81	82	Beat Bugs: All Together Now	2017	0	7	NA	Josh Wakely,Pablo De La Torre	Ashleigh Ball,Lili Beaudoin,Shannon Chan-Kent,...	Animation,Short,Adventure,Comedy,Family,Fantas...	United States	...	NA	NA	tv series	NA	1	0	0	0	1	Netflix
103	104	Amazing Grace	2018	0	7.1	67	Michael Apted	Kate Jenkinson,Sigrid Thornton,Alex Dimitriade...	Drama	Australia	...	The series centres on midwife Grace and her pa...	118	tv series	1	0	1	0	0	1	Hulu

5 rows × 21 columns

df_age_7_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 7]

print('\nTV Shows with 7+ Age Rating are : \n')
df_age_7_tvshows.head(5)

TV Shows with 7+ Age Rating are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
27	28	The Adventures of Sharkboy and Lavagirl	2005	7	8	19	Doug Walker	Doug Walker,Malcolm Ray,Tamara Chambers,Barney...	Comedy,Talk-Show	NA	...	If you thought the Spy Kids sequels were bad.....	29	tv series	NA	1	0	0	0	1	Netflix
45	46	LEGO Jurassic World: The Indominus Escape	2016	7	5.7	NA	NA	A.J. LoCascio,Sendhil Ramamurthy,Fred Tatascio...	Animation,Action,Adventure,Comedy,Family,Sci-Fi	United States	...	Jurassic park founder, Simon Masrani, recruits...	24	tv series	1	1	0	0	0	1	Netflix
76	77	Pac’s Scary Halloween	2016	7	5.4	NA	NA	Ashleigh Ball,Gabriel C. Brown,Ian James Corle...	Animation,Action,Comedy,Drama,Family,Fantasy,S...	United States	...	NA	44	tv series	NA	1	0	0	0	1	Netflix
88	89	EMI	2008	7	4.3	NA	NA	Carmindy,Ted Gibson,Clinton Kelly,Stacy London	Family,Reality-TV	NA	...	NA	44	tv series	NA	1	0	0	0	1	Netflix
99	100	Jake's Buccaneer Blast	2014	7	5.2	NA	NA	Megan Richie,Jadon Sand,Riley Thomas Stewart,D...	Animation	United States	...	NA	NA	tv series	1	1	0	0	0	1	Netflix

5 rows × 21 columns

df_age_13_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 13]

print('\nTV Shows with 13+ Age Rating are : \n')
df_age_13_tvshows.head(5)

TV Shows with 13+ Age Rating are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	...	The gang, 5 raging alcoholic, narcissists run ...	22	tv series	18	1	0	0	0	1	Netflix
30	31	Yol Arkadaşım 2	2018	13	6.4	NA	NA	Bigkem Melisa Özelçi,Asena Keskinci,Serhat Mus...	Drama,Romance	Turkey	...	NA	NA	tv series	2	1	0	0	0	1	Netflix
55	56	The Road to El Camino: Behind the Scenes of El...	2019	13	7.1	NA	NA	Charles Baker,Jonathan Banks,Melissa Bernstein...	Documentary,Short	United States	...	NA	13	tv series	NA	1	0	0	0	1	Netflix
82	83	The Birth Reborn 3	2018	13	NA	NA	NA	Michel Odent	Documentary	NA	...	NA	NA	tv series	NA	1	0	0	0	1	Netflix
102	103	Get Smart	2008	13	8.2	51	Peter Segal	Don Adams,Barbara Feldon,Edward Platt,Robert K...	Action,Adventure,Comedy,Crime,Family,Mystery,S...	United States	...	Maxwell Smart is a bumbling secret agent, assi...	25	tv series	5	0	1	0	0	1	Hulu

5 rows × 21 columns

df_age_16_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 16]

print('\nTV Shows with 16+ Age Rating are : \n')
df_age_16_tvshows.head(5)

TV Shows with 16+ Age Rating are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
54	55	My True Friend	2012	16	7.7	NA	Atsajun Sattakovit	Angelababy,Allen Deng,Yilong Zhu,Kai Tan,Anlia...	Drama,Romance	China	...	A story that revolves around real estate agent...	103	tv series	1	1	0	1	0	1	Netflix
147	148	Dark Money	2018	16	6.6	96	NA	Babou Ceesay,Jill Halfpenny,Susan Wokoma,Olive...	Crime,Drama,Thriller	United Kingdom	...	Life is not always like the movies. Love isn't...	220	tv series	1	0	0	1	0	1	Prime Video
157	158	Halo 4: Forward Unto Dawn	2012	16	6.9	NA	NA	Thom Green,Anna Popplewell,Enisha Brewster,Aye...	Action,Adventure,Family,Sci-Fi,Thriller,War	United States	...	In real life, Robert Oppenheimer was the scien...	100	tv series	1	0	0	1	0	1	Prime Video
161	162	Innocent	2011	16	5.4	64	NA	Mario Casas,Juana Acosta,Josean Bengoetxea,Oli...	Crime,Drama,Mystery,Thriller	Spain	...	Legendary poet, singer/songwriter and alleged ...	45	tv series	1	0	0	1	0	1	Prime Video
178	179	Houdini	2014	16	7.4	NA	NA	Adrien Brody,Kristen Connolly,Evan Jones,Tim P...	Biography,Drama	United States,Canada	...	At the beginning of a nightly Alcoholics Anony...	174	tv series	1	0	0	1	0	1	Prime Video

5 rows × 21 columns

df_age_18_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 18]

print('\nTV Shows with 18+ Age Rating are : \n')
df_age_18_tvshows.head(5)

TV Shows with 18+ Age Rating are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	Set seven years after the world has become a f...	60	tv series	3	1	0	0	0	1	Netflix
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	In this British historical drama, the turbulen...	52	tv series	2	1	0	0	0	1	Netflix
3	4	Amy	2015	18	7	87	NA	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	...	A family drama focused on three generations of...	60	tv series	6	1	0	1	1	1	Netflix
8	9	Quincy	2018	18	7.3	82	NA	Jack Klugman,John S. Ragin,Robert Ito,Joseph R...	Crime,Drama,Mystery,Thriller	United States	...	Quincy and Sam are working as Coroners. Inspec...	60	tv series	8	1	0	0	0	1	Netflix
11	12	Wakefield	2017	18	8.1	72	Robin Swicord	Rudi Dharmalingam,Mandy McElhinney,Geraldine H...	Mystery	Australia	...	NA	106	tv series	1	1	0	0	0	1	Netflix

5 rows × 21 columns

f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_age['Age'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_age['Age'], ax = ax[1])
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Age s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_age_tvshows['Age'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_age_tvshows['Age'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_age_tvshows['Age'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_age_tvshows['Age'][:100], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()

def round_val(data):
    if str(data) != 'nan':
        return round(data)

df_tvshows_age_group['Age Group'] = df_tvshows_age['Age'].apply(round_val)
 
age_values = df_tvshows_age_group['Age Group'].value_counts().sort_index(ascending = False).tolist()
age_index = df_tvshows_age_group['Age Group'].value_counts().sort_index(ascending = False).index
 
# age_values, age_index

age_group_count = df_tvshows_age_group.groupby('Age Group')['Title'].count()
age_group_tvshows = df_tvshows_age_group.groupby('Age Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
age_group_data_tvshows = pd.concat([age_group_count, age_group_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
age_group_data_tvshows = age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

# Age Group with TV Shows Counts - All Platforms Combined
age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
3	16	1018	350	521	236	7
1	7	958	334	372	306	72
4	18	946	497	233	253	1
0	0	521	147	150	207	73
2	13	64	4	9	26	30

age_group_data_tvshows.sort_values(by = 'Age Group', ascending = False)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
4	18	946	497	233	253	1
3	16	1018	350	521	236	7
2	13	64	4	9	26	30
1	7	958	334	372	306	72
0	0	521	147	150	207	73

fig = px.bar(y = age_group_data_tvshows['TV Shows Count'],
             x = age_group_data_tvshows['Age Group'], 
             color = age_group_data_tvshows['Age Group'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'Age : '},
             title  = 'TV Shows with Group Age : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()

fig = px.pie(age_group_data_tvshows,
             names = age_group_data_tvshows['Age Group'],
             values = age_group_data_tvshows['TV Shows Count'],
             color = age_group_data_tvshows['TV Shows Count'],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'TV Shows Count based on Age Group')
fig.show()

df_age_group_high_tvshows = age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_age_group_high_tvshows = df_age_group_high_tvshows.drop(['index'], axis = 1)
# filter = (age_group_data_tvshows['TV Shows Count'] ==  (age_group_data_tvshows['TV Shows Count'].max()))
# df_age_group_high_tvshows = age_group_data_tvshows[filter]
 
# highest_rated_tvshows = age_group_data_tvshows.loc[age_group_data_tvshows['TV Shows Count'].idxmax()]
 
# print('\nAge with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_age_group_high_tvshows.head(5)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	16	1018	350	521	236	7
1	7	958	334	372	306	72
2	18	946	497	233	253	1
3	0	521	147	150	207	73
4	13	64	4	9	26	30

df_age_group_low_tvshows = age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_age_group_low_tvshows = df_age_group_low_tvshows.drop(['index'], axis = 1)
# filter = (age_group_data_tvshows['TV Shows Count'] = =  (age_group_data_tvshows['TV Shows Count'].min()))
# df_age_group_low_tvshows = age_group_data_tvshows[filter]
 
# print('\nAge with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_age_group_low_tvshows.head(5)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	13	64	4	9	26	30
1	0	521	147	150	207	73
2	18	946	497	233	253	1
3	7	958	334	372	306	72
4	16	1018	350	521	236	7

print(f'''
      Total '{df_tvshows_age['Age'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{age_group_data_tvshows['Age Group'].unique().shape[0]}' Age Group, They were Like this, \n
 
      {age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Age Group'].unique()} etc. \n
 
      The Age Group with Highest TV Shows Count have '{age_group_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_age_group_high_tvshows['Age Group'][0]}', &\n
      The Age Group with Lowest TV Shows Count have '{age_group_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_age_group_low_tvshows['Age Group'][0]}'
      ''')

      Total '3507' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '5' Age Group, They were Like this, 

 
      [16  7 18  0 13] etc. 

 
      The Age Group with Highest TV Shows Count have '1018' TV Shows Available is '16', &

      The Age Group with Lowest TV Shows Count have '64' TV Shows Available is '13'
      

netflix_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_age_group_tvshows = netflix_age_group_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_age_group_high_tvshows = netflix_age_group_high_tvshows.drop(['index'], axis = 1)
 
netflix_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_age_group_low_tvshows = netflix_age_group_low_tvshows.drop(['index'], axis = 1)
 
netflix_age_group_high_tvshows.head(5)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	18	946	497	233	253	1
1	16	1018	350	521	236	7
2	7	958	334	372	306	72
3	0	521	147	150	207	73
4	13	64	4	9	26	30

hulu_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_age_group_tvshows = hulu_age_group_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_age_group_high_tvshows = hulu_age_group_high_tvshows.drop(['index'], axis = 1)
 
hulu_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_age_group_low_tvshows = hulu_age_group_low_tvshows.drop(['index'], axis = 1)
 
hulu_age_group_high_tvshows.head(5)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	16	1018	350	521	236	7
1	7	958	334	372	306	72
2	18	946	497	233	253	1
3	0	521	147	150	207	73
4	13	64	4	9	26	30

prime_video_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_age_group_tvshows = prime_video_age_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_age_group_high_tvshows = prime_video_age_group_high_tvshows.drop(['index'], axis = 1)
 
prime_video_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_age_group_low_tvshows = prime_video_age_group_low_tvshows.drop(['index'], axis = 1)
 
prime_video_age_group_high_tvshows.head(5)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	7	958	334	372	306	72
1	18	946	497	233	253	1
2	16	1018	350	521	236	7
3	0	521	147	150	207	73
4	13	64	4	9	26	30

disney_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_age_group_tvshows = disney_age_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_age_group_high_tvshows = disney_age_group_high_tvshows.drop(['index'], axis = 1)
 
disney_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_age_group_low_tvshows = disney_age_group_low_tvshows.drop(['index'], axis = 1)
 
disney_age_group_high_tvshows.head(5)

	Age Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	0	521	147	150	207	73
1	7	958	334	372	306	72
2	13	64	4	9	26	30
3	16	1018	350	521	236	7
4	18	946	497	233	253	1

print(f'''
      The Age Group with Highest TV Shows Count Ever Got is '{df_age_group_high_tvshows['Age Group'][0]}' : '{df_age_group_high_tvshows['TV Shows Count'].max()}'\n
      The Age Group with Lowest TV Shows Count Ever Got is '{df_age_group_low_tvshows['Age Group'][0]}' : '{df_age_group_low_tvshows['TV Shows Count'].min()}'\n
      
      The Age Group with Highest TV Shows Count on 'Netflix' is '{netflix_age_group_high_tvshows['Age Group'][0]}' : '{netflix_age_group_high_tvshows['Netflix'].max()}'\n
      The Age Group with Lowest TV Shows Count on 'Netflix' is '{netflix_age_group_low_tvshows['Age Group'][0]}' : '{netflix_age_group_low_tvshows['Netflix'].min()}'\n
      
      The Age Group with Highest TV Shows Count on 'Hulu' is '{hulu_age_group_high_tvshows['Age Group'][0]}' : '{hulu_age_group_high_tvshows['Hulu'].max()}'\n
      The Age Group with Lowest TV Shows Count on 'Hulu' is '{hulu_age_group_low_tvshows['Age Group'][0]}' : '{hulu_age_group_low_tvshows['Hulu'].min()}'\n
      
      The Age Group with Highest TV Shows Count on 'Prime Video' is '{prime_video_age_group_high_tvshows['Age Group'][0]}' : '{prime_video_age_group_high_tvshows['Prime Video'].max()}'\n
      The Age Group with Lowest TV Shows Count on 'Prime Video' is '{prime_video_age_group_low_tvshows['Age Group'][0]}' : '{prime_video_age_group_low_tvshows['Prime Video'].min()}'\n
      
      The Age Group with Highest TV Shows Count on 'Disney+' is '{disney_age_group_high_tvshows['Age Group'][0]}' : '{disney_age_group_high_tvshows['Disney+'].max()}'\n
      The Age Group with Lowest TV Shows Count on 'Disney+' is '{disney_age_group_low_tvshows['Age Group'][0]}' : '{disney_age_group_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Age Group with Highest TV Shows Count Ever Got is '16' : '1018'

      The Age Group with Lowest TV Shows Count Ever Got is '13' : '64'

      
      The Age Group with Highest TV Shows Count on 'Netflix' is '18' : '497'

      The Age Group with Lowest TV Shows Count on 'Netflix' is '13' : '4'

      
      The Age Group with Highest TV Shows Count on 'Hulu' is '16' : '521'

      The Age Group with Lowest TV Shows Count on 'Hulu' is '13' : '9'

      
      The Age Group with Highest TV Shows Count on 'Prime Video' is '7' : '306'

      The Age Group with Lowest TV Shows Count on 'Prime Video' is '13' : '26'

      
      The Age Group with Highest TV Shows Count on 'Disney+' is '0' : '73'

      The Age Group with Lowest TV Shows Count on 'Disney+' is '18' : '1'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_a_ax1 = sns.barplot(x = netflix_age_group_tvshows['Age Group'], y = netflix_age_group_tvshows['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_a_ax2 = sns.barplot(x = hulu_age_group_tvshows['Age Group'], y = hulu_age_group_tvshows['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_a_ax3 = sns.barplot(x = prime_video_age_group_tvshows['Age Group'], y = prime_video_age_group_tvshows['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_a_ax4 = sns.barplot(x = disney_age_group_tvshows['Age Group'], y = disney_age_group_tvshows['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])
 
plt.show()

plt.figure(figsize = (20, 5))
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('Age Group', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()

print(f'''
      Accross All Platforms Total Count of Age Group is '{age_group_data_tvshows['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Netflix' is '{netflix_age_group_tvshows['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Hulu' is '{hulu_age_group_tvshows['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Prime Video' is '{prime_video_age_group_tvshows['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Disney+' is '{disney_age_group_tvshows['Age Group'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Age Group is '5'

      Total Count of Age Group on 'Netflix' is '5'

      Total Count of Age Group on 'Hulu' is '5'

      Total Count of Age Group on 'Prime Video' is '5'

      Total Count of Age Group on 'Disney+' is '5'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_a_ax1 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_a_ax2 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_a_ax3 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_a_ax4 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])

plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_a_ax1 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_a_ax2 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_a_ax3 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_a_ax4 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])
 
plt.show()